Self-service analytics and the processing of hydrocarbons

This paper describes the use of self-service analytics on time series process data for the troubleshooting and optimization of refinery operations in the context of data visualization principles and best practices. Refining relevant examples are used to demonstrate how end-users can access real-time and historical process data and apply the following analytics operations across several refining functions, including (1) incident troubleshooting - identifying periods of interest and methods available to investigate related plant data, patterns, events and disturbances leading up to the incident, and (2) data cleansing - filtering sensor data to remove outliers and bad quality data, splicing and aligning data streams for more accurate analysis and to improve the confidence in the outputs of subsequent analysis, such as the outputs of multivariate, regression-based system identification. The paper also provides examples of how ad hoc analyses can be scaled up to plantwide analytics and evolve into routine, automated tasks. The importance of analytic provenance and collaboration in sharing new insights from data is also discussed. To address the human factors associated with self-service analytics innovation, the paper concludes with lessons learnt, observations and adaptations compared to the traditional "business-as-usual approaches, best practices for data governance, and the implications for engineers that operate in a safety-critical environment.